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Olfaction

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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Related Experiment Video

Updated: Dec 21, 2025

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research

Author Spotlight: Exploring Olfactory Influences on Corticospinal Excitability - Insights and Innovations in Neurological Research

Published on: January 19, 2024

Decoding olfactory stimuli in EEG data using nonlinear features: A pilot study.

Kiana Ezzatdoost¹, Hadi Hojjati¹, Hamid Aghajan¹

¹Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.

Journal of Neuroscience Methods

|May 20, 2020

Summary

This summary is machine-generated.

Researchers developed a novel olfactory stimulus decoder using nonlinear EEG features. This method accurately identifies perceived odors and their pleasantness, outperforming existing techniques for better insights into odor perception.

Keywords:

Chaotic signal processing Nonlinear EEG processing Odor classification Odor pleasantness Olfactory decoder Olfactory perception

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Last Updated: Dec 21, 2025

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Published on: May 24, 2020

Area of Science:

Neuroscience
Signal Processing

Background:

Decoding visual and auditory stimuli from EEG is established, but olfactory stimulus decoding from EEG is novel.
Olfactory system deficits are linked to neurodegenerative diseases, increasing interest in olfactory processing mechanisms.

Purpose of the Study:

To introduce an olfactory stimulus decoder using nonlinear EEG features.
To classify perceived odors and their pleasantness using EEG data.
To advance understanding of the brain's odor perception processes.

Main Methods:

Utilized nonlinear and chaotic features from electroencephalography (EEG) data.
Developed subject-specific and cross-subject classifiers for odor identification and pleasantness classification.
Classified 4 olfactory stimuli and odor pleasantness in healthy male subjects under eyes-open and eyes-closed conditions.

Main Results:

Achieved high accuracy (96.71% eyes-open, 88.79% eyes-closed) for subject-specific odor identification.
Reached 91.7% (eyes-open) and 82.1% (eyes-closed) accuracy for cross-subject odor pleasantness classification.
Demonstrated above-chance cross-subject classification accuracy for all 4 odors (64.3% eyes-open, 54.8% eyes-closed).

Conclusions:

The proposed nonlinear EEG feature-based method outperforms existing classification schemes in accuracy.
Results facilitate the design of more accurate EEG-based odor detection classifiers.
Findings contribute to a deeper understanding of the neural mechanisms underlying odor perception.